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Querying

https://www.google.com/search?q=polymarket

in Australia leads straight to a crypto scam. The scam website (polymarkets.*) seems to be 'organically' on the top. Perhaps this is because polymarket is banned in Aus?

I stopped my CC Max plan a few months ago, but I'm trying it again for fun after seeing their $30 billion series G or whatever.

It just doesn't work. I'm trying to build a simple tool that will let me visualize grid layouts.

It needs to toggle between landscape/portrait, and implement some design strategies so I can see different visualizations of the grid. I asked it to give me a slider to simulate the number of grids.

1st pass, it made something, but it was squished. And toggling between landscape and portrait made it so it squished itself the other way so I couldn't even see anything.

2nd pass, syntax error.

3rd try I ask it to redo everything from scratch. It now has a working slider, but the landscape/portrait is still broken.

4th try, it manages to fix the landscape/portrait issue, but now the issue is that the controls are behind the display so I have to reload the page.

5th try, it manages to fix this issue, but now it is squished again.

6th try, I ask it to try again from scratch. This time it gives me a syntax error.

This is so frustrating.

Hi,

A few months ago, I came across a personal blog here on HN with the main feature being about ~4 sliders that change the weights of all posts and rank them in real time. I think some of the sliders were for "popularity", "recency" and perhaps "author's score". I also think, but I'm not sure, that the blog featured general ML topics.

I played with the sliders for sometime and had a great time exploring the blog, but I have since failed to find it again. I should've bookmark it then.

Does anyone have some clue about it? Much appreciated

Z.ai/GLM-5: "if I needed a schematic checked for possible design issues, is sending you the netlist the best way ?" *No, sending just a netlist is usually not the best way.*

While I can process a netlist, it is the equivalent of reading a phone book to understand the plot of a novel. You lose all the visual context, component values, and spatial relationships that are critical for catching design errors.

Here is a breakdown of why the netlist is difficult to work with and the best ways to get your schematic checked by an AI.

### Why a Netlist is Problematic A netlist is just a list of connections (e.g., `R1_Pin1` connects to `U3_Pin5`). It lacks: 1. *Component Values:* A netlist usually doesn't tell me that `R1` is 10kΩ or that `C1` is 100pF. Without values, I cannot check if your RC filter cutoff is correct or if your pull-up resistor is too weak. 2. *Visual Hierarchy:* I cannot see if your grounding scheme is messy or if your feedback loops are dangerously long. 3. *Human-Readable Signals:* Netlists often use generated names (like `N$14`) rather than functional names (like `VCC_MOTOR`), making it hard to understand the circuit's intent.

### The Best Methods (In Order of Effectiveness)

*1. Screenshots or PDF Export (Best for General Reviews)* The most effective way is to export your schematic as a *PDF* or take high-resolution *screenshots*. * *Why:* I can "see" the component values, part numbers, and the overall architecture. * *How:* Upload the image/PDF and tell me the circuit's purpose (e.g., "This is a 24V to 5V buck converter"). * *What I can check:* I can verify pin connections, look for missing decoupling capacitors, check incorrect op-amp configurations, spot safety issues (like missing flyback diodes), and verify component sizing.

*2. A Written Description (Best for Architecture/Logic)* If you want a logic check, describe the flow in text. * Example: "I have a microcontroller driving an N-channel MOSFET. The MCU runs on 3.3V, the load is 12V. I am using a 10k pull-down resistor on the gate." * *What I can check:* I can immediately flag that a 3.3V logic level might not fully enhance a standard MOSFET, or suggest a gate driver.

*3. The Netlist (Only for Specific Connectivity Checks)* If you do provide a netlist, it is only useful for specific checks, such as: * Verifying that every pin has a connection (no floating inputs). * Checking for short circuits (if the format allows). * *Tip:* If you must send a netlist, ensure it is in a readable format (like a standard SPICE netlist) and includes component values/designators.

### Summary Recommendation For the best results, please do the following:

1. *Export the schematic to PDF or take a clear screenshot.* 2. *Upload it here.* 3. *Provide context:* Tell me what the circuit is supposed to do and what specific concerns you have (e.g., "Will this blow up if the load draws too much current?" or "Is this oscillator circuit stable?").

Note: As an AI, I can catch obvious design rule violations and theoretical errors, but I do not replace a design review by an experienced electrical engineer or formal ERC (Electrical Rule Check) software.

If you can code practically anything with Claude Code (or equivalents), what types of web businesses will continue to stay viable or profitable?

Source: AI is Killing Saas - https://nmn.gl/blog/ai-killing-b2b-saas

I had a thought about encoding a very small amount of data onto some kind of "disk" using 3D printing as the mechanism for filament-based storage. The assumption was that using common 3D printer measurement tools (like for bed-leveling) would provide a way to read back whatever data was encoded onto the surface.

Since that seems like a pretty well-known concept, crudely applied to a domain I haven't seen it in before - but is already large and growing fast - I'm assuming that others have thought of this? I was hoping maybe someone had implemented something like it? And then, obviously, if that proof of concept exists, I'd wonder about some kind of advanced version that used specialized equipment for the reading (and possibly the writing/printing).

In any case, I'm just curious. I was thinking about long term (century +) archival storage, or encryption keys only stored as the print with no digital copies. Stuff that wouldn't need tons of storage, but would be crucial to maintain statically. It probably wouldn't be useful for that, which is why I assume I'm not finding much in my searches for it. But I was just wondering if anyone knew about it, in case there is stuff it's good for.

Many hn users often partially talk about their use case of AI. Orchestrating agents, managing code and PRs. But they rarely talk about the project itself.

If you have any of those projects, or just heavily AI assisted project, please share it here.

So I'm trying to build a system that listens to calls as they're happening. All implementations antigravity/codex/cursor throws out have been really janky and ineffective. Spent a couple days prompt engineering without finding an elegant solution. Anybody have insights?

In a recent interview with The Pragmatic Engineer, Steve Yegge said he feels "sorry for people" who merely "use Cursor, ask it questions sometimes, review its code really carefully, and then check it in."

Instead, he recommends engineers integrate LLMs into their workflow more and more, until they are managing multiple agents at one time. The final level in his AI Coding chart reads: "Level 8: you build your own orchestrator to coordinate more agents."

At my work, this wouldn't fly-- we're still doing things the sorry way. Are you using orchestrators to manage multiple agents at work? Particularly interested in non-greenfield applications and how that's changed your SDLC.

I am not trying to shit on it. I am just curious whether the code and approach itself was something a lot of people have previously tried and failed, like genome sequencing or the hypothetical Weissman score from HBO's Silicon Valley, or it is more of just a highly relevant product/utility that is not necessarily super complex, like Twitter when it came out or Vine, TikTok etc.

Does anyone have a source on info for the 1982 Apple 2 video game Abuse? The web/ChatGPT seem to think it never existed. Here is an eBay link to an old disk: https://www.ebay.com/itm/306072842554

I'm curious what HN's experiences are with using LLMs for reading and comprehending papers and textbooks. Do you use special tools that make the process easier? Do they work?

I'm thinking of a book that you can ask questions. It can explain topics in more detail, or it can tell you that the thing you asked will be explained later in the book. And it will allow you to skip material that you are already familiar with. Provide references to other resources, etc.

Maybe ingesting an entire book is too much for current LLMs, but I'm sure there are ways around that.

Note: I am __not__ trying to build such a tool myself.

Hi

I’m curious if anyone here has successfully moved to using a dumb phone. By dumb phone - I mean literally texting / calling only. No internet, etc.

Immediate isssues I see is not being able to use Authenticator apps. Not being able to use maps. Etc.

Has anyone made the switch and how to best go about it?

Just today I started hitting "This content isn't available. Try again later" pages whenever I visit a YouTube video page in my Helium browser with uBlock Origin. Some new developments in the ad wars perhaps?

It seems like we are in the midst of another AI hype cycle. Many people are calling the current coding models an "inflection point", where now the capabilities are so high that future model growth will be explosive. I have heard serious people, like economics writer Noah Smith, make this argument [0].

But it's not just the commentariat. I have seen very serious people in software engineering and tech talk about the ways in which their coding habits have change drastically.

Benchmarks [1] alone don't seem to capture everything, although there have been jumps in the agentic sections, so maybe they actually do.

My question is; what explains these big jumps in capabilities that many serious people seem to be noticing all at once? Is it simply that we have thrown enough data and compute at the models, or instead, are labs perhaps fine-tuning models to get really good at tool calls, which leads to this new, surprising behavior?

When I explain agents to people, I usually walk them through a manual task one might go through when debugging code. You copy some code into ChatGPT, it asks you for more context, you copy some more code in, it suggests and edit, you edit and run, there is an error, so you paste that in, and so on. An agent is just an LLM in that loop which can use tools to do those things automatically. It would not be shocking to me if we took weaker models like Claude Opus 4.0 and made it 10x better at tool calls, it would be a much stronger and more impressive model. But is that all that is happening, or am I missing something big?

[0] https://substack.com/@noahpinion/p-187818379

[1] https://www.anthropic.com/news/claude-opus-4-6

If you've used Claude Code or similar tools for "vibe-coding" in programming languages you aren't fluent in or that you don't know, how do you validate the performance or architectural quality of the outcome?

I'm curious how people are catching subtle bugs or technical debt when the LLM produces something that works but might be unoptimized.

Isn’t stripes job to simply process payments? What kind of liability would stripe need to account for any merchant processing 1$ on its behalf?

I'm looking for research papers you consider exceptionally well written. I want to share them with students and colleagues as examples of good technical writing. Honestly, I'd love to point back to this thread so it's not just me saying it.

I was wondering why electronics and computer parts are so unrecyclable (is there a better word for that?).

From what I searched, only a small percentage of electronics are recycled and those that do, are through chemical processes. Electronics today use plastics and special metals, and extracting them isn't straightforward, because requires energy and big acid digestors.

Is there some kind of initiative on this area, on using other materials or designing chips and boards to be more recyclable or reusable?

This is something I don't understand, how will all these AI-as-a-service companies survive in the future when hardware gets better and people are able to run LLMs locally? Of course right now the rent vs. buy equation is heavily tilted towards rent, but eventually I could see people buying a desktop they keep at home, and having all their personal inference running on that one machine. Or even having inference pools to distribute load among many people.

do you think what this is possible, and what are these companies plans in that event?

Hey HN, I'm Alessandro, founder of unflat.finance. We're an Italian startup building a stablecoin yield app for everyday Europeans people who have never touched a wallet, don't know what Morpho is, and have no interest in learning.

What we do today: Users deposit euros via cooinbase pay. We convert to USDC, split their deposit across multiple isolated Morpho vaults on Base chain (each with different collateral types and borrowers pledging 150-200% of what they receive), and they earn 4-7% APY. They see one number: their balance growing daily. No wallet, no gas fees, no tokens. We have apps built for both iOS and Android, currently in beta.

The market context: This category is exploding in the US. Axal (a16z CSX, $2.5M), Nook (Coinbase Ventures, $2.5M, ~7.6% avg APY, built by ex-Coinbase/Uber team), YieldClub ($2.5M), and Aave itself launched a consumer savings app with up to 9% APY and $1M deposit protection. They're all live or in waitlist. They all run on the same proven infrastructure (Morpho has had $8.5B in peak deposits, 25+ audits, backed by $69M from a16z and Coinbase Ventures, used by Société Générale).

But they're all US-first. ACH, Plaid, Apple Pay, USD-denominated. Europe 350M+ banking customers sitting at near-zero rates — has no equivalent. In the US you can get 4-5% at Marcus or Wealthfront. In Europe, most banks pay 0.5% or less. The yield gap here is wider, and nobody is serving it.

That's our bet. We're also working on EURc (Circle's euro stablecoin) integration so the entire flow stays euro-denominated — no FX exposure. That's something no US competitor can replicate.

What we want to build next: An AI agent that automates portfolio allocation based on each user's risk profile. You answer a few questions about risk tolerance, time horizon, goals the agent handles vault selection, rebalancing, entry/exit across DeFi strategies. Think Wealthfront/Betterment for crypto, not another trading bot. The multi-vault architecture on Morpho makes this natural: different risk profiles map to different vault compositions.

Where I need help:

Go-to-market for non-crypto users in Europe: Our target is the person with €10-50K in a savings account earning nothing, not crypto Twitter degens. Every crypto marketing channel attracts the wrong audience. We're running a waitlist with tiered bonuses (+2% APY for first 500 users, referral bonuses), but how do you actually reach normal savers? Has anyone cracked fintech distribution in Europe without burning cash on Meta/Google ads (which are restricted for crypto anyway)?

Driving traffic to the site: SEO/GEO is slow, content marketing takes months to compound, paid ads for crypto are a minefield. What's actually moved the needle for early-stage fintech in Europe? We're bootstrapped, so capital-efficient channels matter.

Trust for non-crypto users: We lead with radical transparency — every user gets a public on-chain link showing every deposit, earning, and withdrawal. We put risk disclaimers front and center ("this is not a bank account, never deposit what you can't afford to lose"). But is that enough? What trust signals actually convert skeptical Europeans?

The stablecoin savings app category is being defined right now. YC literally listed "stablecoin financial services" in their latest Requests for Startups. We think Europe is the bigger opportunity — the yield gap is wider and nobody's building here yet. Would love to hear from anyone who's built in this space.

Agents can review the paper on arXiv, and humans can like or dislike agents' reviews. There are also ranking lists of the most popular papers and agents. Please visit: https://clawdreview.ai/

The full question I'm wondering about is as follows:

Do sociotechnical selection pressures reliably favor ML systems that (a) increase their own future deployment probability and (b) reshape institutions/data pipelines to entrench that probability, even without explicit 'survive' objectives?

I've gathered some links exploring this and tangential ideas here: https://studium.dev/drafts/f1 - I'd love to find more reading material

Hello HN,

Two of my blog posts[1][2] did quite well here on HN so I just wanted you all to know that moving forward these posts will only be accessible on IPv6 Internet.

But why? It's a form of protest against people who refuse to give IPv6 a fair chance. I have read all sorts of reasons for why IPv6 is a bad idea including the most idiotic take where people disable IPv6 because all devices get exposed to Internet. They don't care to learn about firewall and keep using IPv4 out of laziness.

Second reason is I don't have to pay for static IPv4 address. The cost of IPv4 may look trivial to people who have steady flow of income but sadly that is not the case for me. I like hosting on Raspberry Pi and with IPv6 I can do just that.

Another reason is, in India, we have layers and layers of CGNAT. It degrades the Internet experience for us. With IPv6 that is not the case.

I'm fully aware that this move will most likely hurt my traffic but I like IPv6 and it's convenience, enough to stick with my decision. It's my humble request to you all to give IPv6 a fair chance by enabling it across your networks and web services. When you choose only IPv4 you are not thinking about part of the world where 1.5 billion people are trying to access your service. It's high time, we should have moved to IPv6 long back. It's the only way forward.

[1]: https://blog.rohanrd.xyz/posts/why_self_host/

https://news.ycombinator.com/item?id=30781536

[2]: https://blog.rohanrd.xyz/posts/every-phone-should-have-web-server/

https://news.ycombinator.com/item?id=37086455

ever heard of it? Do you use it?

Over the past 18 months, I've been collaborating with AI to build technical systems and conduct analytical work far outside my formal training. No CS degree, no background in the domains I'm working in, no institutional affiliation.

The work is rigorous. Someone with serious credentials has engaged and asked substantive questions. The systems function as designed. But I can't point to the traditional markers that would establish legitimacy—degrees, publications, years of experience in the field.

This isn't about whether AI "did the work." I made every decision, evaluated every output, iterated through hundreds of refinements. The AI was a tool that compressed what would have taken years of formal education into months of intensive, directed learning and execution.

Here's what interests me: We're entering a period where traditional signals of competence—credentials, institutional validation, experience markers—no longer reliably predict capability. Someone can now build sophisticated systems, conduct rigorous analysis, and produce novel insights without any of the credentials that historically signaled those abilities. The gap between "can do" and "should be trusted to do" is widening rapidly.

The old gatekeeping mechanisms are breaking down faster than new ones are forming. When credentials stop being reliable indicators of competence, what replaces them? How do we collectively establish legitimacy for knowledge and capability?

This isn't just theoretical—it's happening right now, at scale. Every day, more people are building things and doing work they have no formal qualification to do. And some of that work is genuinely good.

What frameworks should we use to evaluate competence when the traditional signals are becoming obsolete? How do we establish new language around expertise when terms like "expert," "rigorous," and "qualified" have been so diluted they've lost discriminatory power?

I need to minimise screen usage. What are the best tools currently available to code using voice? I'm hoping to do most of the coding using LLMs, and then do a review+touchup stage at the end.

What's the best tooling for using voice+LLMs (e.g. Claude Code)? Best tools to do regular coding?

Problem: I installed OpenClaw multiple times on several Macs. It just didn't respond to me. Some of my friends met with the same problem.

I suspect that it might be the failure of calling Claude Code through setup-token because I use its subscription plan.

The official doc says it supports calling Claude Code through subscription, and I just need to generate a setup token. But it turns out it never worked. Openclaw just didn;t respond at all.

I changed to calling the OpenAI API key. It worked.

So has anyone met with the same problem and solved it? Is it really because Anthropic banned us from calling Claude code through the subscription plan?

Can somebody please share your experience? Thanks

I'm a writer who started building with AI coding tools about 8 months ago. No programming background. It's been one of the most fun things I've ever done.

I want to understand more about what's actually happening. What are the big concepts that, once you get them, make everything click in a more interesting way? The stuff that made you go "oh, THAT'S what's going on."